WO2020154885A1 - Procédé, appareil, dispositif et support de stockage pour la détection d'un type de cellule individuelle - Google Patents

Procédé, appareil, dispositif et support de stockage pour la détection d'un type de cellule individuelle Download PDF

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Publication number
WO2020154885A1
WO2020154885A1 PCT/CN2019/073647 CN2019073647W WO2020154885A1 WO 2020154885 A1 WO2020154885 A1 WO 2020154885A1 CN 2019073647 W CN2019073647 W CN 2019073647W WO 2020154885 A1 WO2020154885 A1 WO 2020154885A1
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expression
entropy
data set
cell
data
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PCT/CN2019/073647
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English (en)
Chinese (zh)
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李辰威
刘宝琳
康博熙
刘烨丹
任仙文
张泽民
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北京大学
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Priority to PCT/CN2019/073647 priority Critical patent/WO2020154885A1/fr
Priority to CN201980000101.XA priority patent/CN109891508B/zh
Publication of WO2020154885A1 publication Critical patent/WO2020154885A1/fr

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    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B25/00—ICT specially adapted for hybridisation; ICT specially adapted for gene or protein expression
    • G—PHYSICS
    • G16—INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR SPECIFIC APPLICATION FIELDS
    • G16B—BIOINFORMATICS, i.e. INFORMATION AND COMMUNICATION TECHNOLOGY [ICT] SPECIALLY ADAPTED FOR GENETIC OR PROTEIN-RELATED DATA PROCESSING IN COMPUTATIONAL MOLECULAR BIOLOGY
    • G16B30/00—ICT specially adapted for sequence analysis involving nucleotides or amino acids
    • G16B30/10—Sequence alignment; Homology search
    • Y—GENERAL TAGGING OF NEW TECHNOLOGICAL DEVELOPMENTS; GENERAL TAGGING OF CROSS-SECTIONAL TECHNOLOGIES SPANNING OVER SEVERAL SECTIONS OF THE IPC; TECHNICAL SUBJECTS COVERED BY FORMER USPC CROSS-REFERENCE ART COLLECTIONS [XRACs] AND DIGESTS
    • Y02—TECHNOLOGIES OR APPLICATIONS FOR MITIGATION OR ADAPTATION AGAINST CLIMATE CHANGE
    • Y02A—TECHNOLOGIES FOR ADAPTATION TO CLIMATE CHANGE
    • Y02A90/00—Technologies having an indirect contribution to adaptation to climate change
    • Y02A90/10—Information and communication technologies [ICT] supporting adaptation to climate change, e.g. for weather forecasting or climate simulation

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  • the embodiments of the present invention relate to the field of single-cell transcriptome sequencing data analysis, and in particular to a single-cell type detection method, device, equipment and storage medium.
  • the interpretability of the selected genes is poor, and it is necessary to use biological knowledge to obtain results based on algorithms
  • the marker gene annotates the taxa.
  • the above existing analysis methods all put high requirements on the user's biological background and computing hardware.
  • the invention provides a single cell type detection method, device, equipment and storage medium, which improves the analysis efficiency and accuracy of single cell expression data, and realizes rapid and accurate detection of cell types.
  • an embodiment of the present invention provides a single cell type detection method, including:
  • the reference data is input into the expression entropy model to determine the information genes contained in each type of cell in the reference data; the reference data includes the expression profile data set of M genes in N single cells; the expression entropy model is trained The reference data is obtained;
  • the cell type of the single cell to be tested is determined according to the occurrence probability and the expression level.
  • the method before inputting the reference data into the expression entropy model to determine the information genes contained in each type of cell in the reference data, the method further includes:
  • the expression entropy is the degree of dispersion of messenger ribonucleic acid expression
  • the inputting the reference data into the expression entropy model to determine the information genes contained in each type of cell in the reference data includes:
  • the training the expression entropy model according to the first expression entropy data set to complete the construction of the expression entropy model includes:
  • the expression entropy model is constructed according to the adjusted reference coefficients.
  • the method further includes:
  • the performing gene screening according to the first expression entropy data set and the second expression entropy data set to determine the information genes contained in each type of cell in the reference data includes:
  • X difference values are selected from the difference value set, and genes corresponding to the X difference values are used as information genes contained in each type of cell in the reference data.
  • an embodiment of the present invention also provides a single cell type detection device, including:
  • the information gene determination module is used to input reference data into the expression entropy model to determine the information genes contained in each type of cell in the reference data;
  • the reference data includes the expression profile data set of M genes in N single cells;
  • the expression entropy model is trained and generated according to the reference data;
  • a probability calculation module which is used to calculate the occurrence probability of the information gene in each type of cell
  • the cell type determination module is configured to determine the cell type of the single cell to be tested according to the occurrence probability and the expression amount when the expression level corresponding to the information gene obtained by detecting the single cell to be tested is received.
  • the device further includes:
  • the expression entropy calculation module is configured to perform expression entropy calculation according to the gene expression amount data set to generate a first expression entropy data set; the expression entropy is the degree of dispersion of gene expression of messenger ribonucleic acid;
  • the model construction module is configured to train the expression entropy model according to the first expression entropy data set, and complete the construction of the expression entropy model.
  • an embodiment of the present invention also provides a device, the device including:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the single cell type detection method provided in the first aspect.
  • an embodiment of the present invention also provides a storage medium, the storage medium includes a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute the order described in the first aspect. Cell type detection method.
  • reference data is input into an expression entropy model to determine the information genes contained in each type of cell in the reference data; the expression entropy model passes The reference data is obtained by training; the occurrence probability of the information gene in each type of cell is calculated; when the corresponding expression amount of the information gene obtained by the detection of the single cell to be tested is received, according to the occurrence probability And the expression amount to determine the cell type of the single cell to be tested.
  • Figure 1 is a flow chart of a single cell analysis method in the prior art
  • FIG. 2 is a schematic flowchart of a first embodiment of a single cell type detection method according to an embodiment of the present invention
  • FIG. 3 is a schematic flowchart of a second embodiment of a single cell type detection method according to an embodiment of the present invention.
  • FIG. 4 is a schematic flowchart of a third embodiment of a single cell type detection method according to an embodiment of the present invention.
  • FIG. 5 is a schematic flowchart of a fourth embodiment of a single cell type detection method according to an embodiment of the present invention.
  • FIG. 6 is a schematic flowchart of a fifth embodiment of a single cell type detection method according to an embodiment of the present invention.
  • FIG. 7 is a schematic structural diagram of a single cell type detection device according to an embodiment of the present invention.
  • Fig. 8 is a schematic structural diagram of a device according to an embodiment of the present invention.
  • the present invention provides a single cell type detection method. By constructing and using an expression entropy model, the analysis efficiency and accuracy of single cell expression data are improved, and rapid Accurately detect cell types.
  • FIG. 2 it is a schematic flowchart of the first embodiment of the single cell type detection method according to the embodiment of the present invention.
  • This embodiment is applicable to single-cell transcriptome sequencing data analysis, and the method can be executed by a processor.
  • the single cell type detection method provided by the embodiment of the present invention further includes the construction process of the expression entropy model.
  • the construction process of the expression entropy model includes:
  • the reference data includes an expression profile data set of M genes in N single cells; the expression entropy model is obtained by training the reference data.
  • the reference data is data generated by a large number of different sequencing platforms (Smart-seq2, 10X genomics, etc.), including 26 published single cell expression profile data sets. Due to the inconsistent measurement standards used in data from different platforms, the expression profile data set needs to be standardized, so that the expression profile data set uniformly uses TPM (Transcripts Per Million) as a measure of gene expression to obtain gene expression Quantity data set.
  • TPM Transcripts Per Million
  • the expression entropy describes the discrete degree of mRNA (messenger ribonucleic acid) expression.
  • the expression of each gene is divided into a bin every 120TPM, so that the expression of each gene in the gene expression data set is divided into different bins, and it is considered that the cells corresponding to the genes in the same bin are in The gene has the same expression level.
  • the calculation method of expression entropy is:
  • S is the expression entropy
  • b k is the number of cells in the Kth bin.
  • the first expression entropy data set is generated according to the number of cells contained in each bin after the gene expression data set is divided into the expression entropy calculation formula for calculation.
  • the construction of the expression entropy model is completed by training the first expression data set.
  • the process of training the first expression data set and constructing the expression entropy model includes:
  • the average gene expression E m of the M genes in the reference data is calculated according to the total expression of the M genes in the gene expression data set.
  • S320 Perform regression analysis on the first expression entropy data set and the average gene expression, and adjust the reference coefficient of the expression entropy model;
  • FIG. 4 is a schematic flowchart of a third embodiment of a single cell type detection method according to an embodiment of the present invention. This embodiment is suitable for single-cell transcriptome sequencing data analysis. Further, after the expression entropy model is constructed, the process of single-cell type detection through the expression entropy specifically includes the following steps:
  • the reference data is input into the expression entropy model to achieve more biologically meaningful gene screening.
  • the process of inputting reference data into the expression entropy model, and determining the information genes contained in each type of cell in the reference data is:
  • E mi is the average expression level of information gene i in the j-th cell.
  • S430 When receiving the expression level corresponding to the information gene obtained by detecting the single cell to be tested, determine the cell type of the single cell to be tested according to the occurrence probability and the expression level.
  • the probability that the single cell to be tested belongs to each type of cell type is calculated according to the expression level and the occurrence probability of the information gene in each cell type; Among them, the formula for calculating the probability that the single cell to be tested belongs to each type of cell type is:
  • E i is the expression level corresponding to the information gene of the single cell to be tested (log2[TPM+1]).
  • the probability set that the single cell to be tested belongs to each type of cell type is calculated, the cell type corresponding to the highest probability value (ie, the highest P j ) in the probability set is the cell type of the single cell to be tested.
  • Determine the information gene contained in each cell type in the reference data by inputting reference data to the expression entropy model, and calculate the probability of the information gene in each cell type, and finally calculate the received single cell to be tested belongs to each type of cell
  • the probability of the type determine the cell type of the single cell to be tested, and realize the rapid definition of the single cell to be tested into the existing cell types. There is no need to perform the tedious existing single cell analysis process, and the type of each cell is directly given. It greatly saves time and resources for single-cell data analysis.
  • the single cell type detection method inputs reference data into an expression entropy model to determine the information genes contained in each type of cell in the reference data; the expression entropy model is trained The reference data is obtained; the occurrence probability of the information gene in each type of cell is calculated; when the corresponding expression amount of the information gene obtained by the detection of the single cell to be tested is received, according to the occurrence probability and the The expression level determines the cell type of the single cell to be tested.
  • Fig. 5 is a schematic flowchart of a fourth embodiment of a single cell type detection method according to an embodiment of the present invention.
  • this embodiment adds a screening method of inputting reference data into an expression entropy model to achieve gene screening.
  • the present invention performs unsupervised gene screening based on the expression entropy model, and the specific steps include:
  • the first expression entropy data set is calculated based on the number of cells contained in each bin after the gene expression data set is divided into the expression entropy calculation formula to generate the first expression entropy data set; the second expression entropy data set To input the reference data into the second expression entropy data set corresponding to the M genes generated in the expression entropy model. Obtain the first expression entropy data and the second expression entropy data corresponding to each of the M genes.
  • the first expression entropy data and the second expression entropy data of each gene are calculated by the above formula to obtain the difference set of M genes.
  • S530 Select X difference values from the difference value set according to the selection rule, and use genes corresponding to the X difference values as information genes contained in each type of cell in the reference data.
  • the user can select the first X differences with the largest d s from the difference set according to requirements, and use the genes corresponding to these X differences as the information genes contained in each type of cell in the reference data.
  • the present invention performs supervised gene screening E-test based on the expression entropy model.
  • the specific steps include: using entropy subtraction as a statistic to perform supervised gene selection.
  • the entropy reduction of each gene is defined as:
  • E m1 represents the average expression of gene i in T1 cells
  • E m2 represents the average expression of gene i in T2 cells. Therefore, for a more appropriate cell type, the entropy reduction of each gene is defined as:
  • the average expression data set of multiple cell types contained in each gene in the reference data is calculated by the above formula to obtain the difference set of M genes; the user can select the top X with the largest d s from the difference set according to the needs
  • the genes corresponding to these X differences are used as the information genes contained in each type of cell in the reference data.
  • Fig. 6 is a schematic flowchart of a fifth embodiment of a single cell type detection method according to an embodiment of the present invention.
  • this embodiment adds an application scenario of unsupervised gene screening.
  • the present invention performs unsupervised gene screening based on the expression entropy model to determine the purity of a type of cell, and the specific steps include:
  • S610 When receiving genetic data obtained by detecting a single cell to be tested, input the genetic data into the expression entropy model to obtain a virtual expression entropy data set;
  • S620 Perform expression entropy calculation according to the gene data to generate an actual expression entropy data set
  • S630 Perform calculation according to the virtual expression entropy data set and the actual expression entropy data set to determine the purity of the cell to be tested.
  • the average expression of genes in the gene data is input into the expression entropy model to obtain a virtual expression entropy data set, that is, the expression entropy S′ i ; the expression is performed according to the gene data
  • Entropy calculation obtains the actual expression entropy data set, that is, the standardized expression entropy S i of genes.
  • the calculation formula for determining the cell purity is:
  • S i is the result of the expression normalized entropy
  • S 'i by the average expression level of gene expression obtained into an entropy formula.
  • FIG. 7 it is a schematic structural diagram of a single cell type detection device according to an embodiment of the present invention.
  • the present invention also provides a single cell type detection device, which can be adapted to perform the single cell type detection method of any one of the first to third embodiments, and the device includes:
  • the information gene determination module 701 is configured to input reference data into an expression entropy model to determine the information genes contained in each type of cell in the reference data;
  • the reference data includes an expression profile data set of M genes in N single cells;
  • the expression entropy model is trained and generated according to the reference data;
  • the probability calculation module 702 is configured to calculate the occurrence probability of the information gene in each type of cell
  • the cell type determination module 703 is configured to determine the cell type of the single cell to be tested according to the occurrence probability and the expression amount when the expression level corresponding to the information gene obtained by detecting the single cell to be tested is received.
  • the device further includes:
  • the data standardization module 704 is used to standardize the reference data to obtain a gene expression data set
  • the expression entropy calculation module 705 is configured to perform expression entropy calculation according to the gene expression amount data set to generate a first expression entropy data set; the expression entropy is the degree of dispersion of gene expression of messenger ribonucleic acid;
  • the model construction module 706 is configured to train the expression entropy model according to the first expression entropy data set, and complete the construction of the expression entropy model.
  • the single cell type detection device inputs reference data into an expression entropy model to determine the information genes contained in each type of cell in the reference data; the expression entropy model is trained The reference data is obtained; the occurrence probability of the information gene in each type of cell is calculated; when the corresponding expression amount of the information gene obtained by the detection of the single cell to be tested is received, according to the occurrence probability and the The expression level determines the cell type of the single cell to be tested.
  • An embodiment of the present invention also provides a device, which includes:
  • One or more processors are One or more processors;
  • Storage device for storing one or more programs
  • the one or more processors When the one or more programs are executed by the one or more processors, the one or more processors implement the single cell type detection method in any one of the first to third embodiments.
  • FIG. 8 it is a schematic structural diagram of a device provided by Embodiment 5 of the present invention.
  • the device includes a processor 801 and a storage device 802; the number of processors 801 in the device may be one or more, as shown in FIG.
  • a processor 801 is taken as an example; the processor 801 and the storage device 802 in the device may be connected by a bus or other methods. In FIG. 8, a bus connection is taken as an example.
  • the storage device 802 can be used to store software programs, computer-executable programs, and modules, such as program instructions/modules corresponding to the command processing method in the embodiment of the present invention (for example, the information gene determination module 701, Probability calculation module 702, cell type determination module 703, data standardization module 704, expression entropy calculation module 705, and model construction module 706).
  • the processor 801 executes various functional applications and data processing in the device by running software programs, instructions, and modules stored in the storage device 802, that is, realizing the above-mentioned command processing method.
  • the embodiment of the present invention also provides a storage medium, the storage medium includes a stored computer program, wherein, when the computer program is running, the device where the storage medium is located is controlled to execute any one of Embodiment 1 to Embodiment 3 Single cell type detection method.
  • processor-executable instruction storage medium provided by the embodiment of the present invention is not limited to the method operations described above, and can also perform the single-cell type detection provided by any embodiment of the present invention. Related operations in the method.
  • the single-cell type detection method, device, device, and storage medium input reference data into an expression entropy model to determine the information genes contained in each type of cell in the reference data;
  • the expression entropy model is obtained by training the reference data; calculating the occurrence probability of the information gene in each type of cell; when receiving the expression amount corresponding to the information gene obtained by detecting the single cell to be tested, The cell type of the single cell to be tested is determined according to the occurrence probability and the expression amount.
  • the present invention can be implemented by software and necessary general-purpose hardware. Of course, it can also be implemented by hardware, but in many cases the former is a better implementation. .
  • the technical solution of the present invention can be embodied in the form of a software product, which can be stored in a computer readable storage medium, such as a computer floppy disk. , Read-Only Memory (ROM), Random Access Memory (RAM), Flash memory (FLASH), hard disk or optical disk, etc., including several instructions to make a computer device (which can be a personal computer) , A server, or a network device, etc.) execute the method described in each embodiment of the present invention.
  • ROM Read-Only Memory
  • RAM Random Access Memory
  • FLASH Flash memory
  • the included units and modules are only divided according to functional logic, but are not limited to the above division, as long as the corresponding function can be realized;
  • the specific names of each functional unit are only used to distinguish each other, and are not used to limit the protection scope of the present invention.

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Abstract

L'invention concerne un procédé, un appareil, un dispositif et un support de stockage permettant de détecter un type de cellule individuelle. Le procédé consiste à : entrer des données de référence dans un modèle d'entropie d'expression et déterminer les gènes d'information contenus dans chaque type de cellule dans les données de référence, les données de référence comprenant un ensemble de données de profilage d'expression pour M gènes dans N cellules individuelles, et le modèle d'entropie d'expression étant obtenu par entraînement des données de référence ; calculer la probabilité d'occurrence des gènes d'information dans chaque type de cellule ; lors de la réception d'une valeur d'expression qui correspond aux gènes d'information obtenus au moyen de la détection d'une cellule individuelle à détecter, en fonction de la probabilité d'occurrence et de la valeur d'expression, déterminer un type de cellule pour la cellule individuelle à détecter.
PCT/CN2019/073647 2019-01-29 2019-01-29 Procédé, appareil, dispositif et support de stockage pour la détection d'un type de cellule individuelle WO2020154885A1 (fr)

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CN201980000101.XA CN109891508B (zh) 2019-01-29 2019-01-29 单细胞类型检测方法、装置、设备和存储介质

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